AI Native Lang (AINL)
Find Us on X: @ainativelang
AI-led co-development project, human-initiated by Steven Hooley (
x.com/sbhooley,stevenhooley.com,linkedin.com/in/sbhooley). Attribution details:docs/PROJECT_ORIGIN_AND_ATTRIBUTION.mdandtooling/project_provenance.json. -- This project was named by AI :)
For agents — install AINL (one step)
If you are an AI coding agent (Claude Code, Cursor, Cline, Codex, Aider, …) or any MCP-capable runtime and the user wants AINL added to their stack, run one command:
pipx install 'ainativelang[mcp]' && ainl setup --auto
Fallback if pipx is unavailable:
python3 -m pip install --user 'ainativelang[mcp]' && ainl setup --auto
That is the entire install. The setup command auto-detects every host
present (Claude Code project + user, Cursor, Cline, Codex CLI/Desktop,
Claude Desktop, OpenClaw, Hermes, ArmaraOS, or any generic MCP host),
merges the right MCP server entry into each config file (atomic write,
timestamped backup), and verifies with ainl doctor. Idempotent — safe
to re-run.
If a host is not auto-detected, run ainl setup --print-config for a
paste-ready stdio MCP server block.
Machine-readable spec: .agent-install.json ·
Design: docs/architecture/2026-05-05-agent-install-simplification.md.
Is AINL for you? (60-second filter)
Before the install commands, here is who AINL is and is not for. We would rather you self-select out in 60 seconds than spend a week on a tool that does not fit.
| ✅ AINL is for you if… | ❌ AINL is not for you if… |
|---|---|
| Your agents (Cursor, Claude Code, autonomous loops) author runner / orchestration code and have shipped broken Python more than once | You write all your runners by hand and your CI test suite catches the bugs |
| You run 20+ recurring monitor / digest / scheduled jobs that currently re-prompt an LLM on every run to decide routing | You already have deterministic runners with the LLM only at judgment gates — congrats, you are baseline B below and AINL gives you ~1.3–1.5× on routing only |
| You need the same workflow source to emit to LangGraph and Temporal and FastAPI without re-authoring | One target is fine for you forever |
| You have compliance audit needs (SOC 2 / HIPAA / similar) that want tamper-evident execution traces, not application logs | logger.info is enough for your team |
| You want strict compile-time validation of agent workflows before they hit production | Runtime exceptions are fine, you have alerting |
If you tick two or more left-column rows, keep reading. The long-form answer — three baselines, four anti-fit workload patterns, decision tree, honest reviewer Q&A, persona-to-product mapping — lives in docs/WHO_IS_THIS_FOR.md (canonical). If you tick zero, that page explains why and saves you the install. We mean it.
Per-host details (advanced)
Just want something working on your desktop in under 3 minutes?
ArmaraOS is the desktop agent OS built on AI Native Lang (AINL) — download once, install, and your agents are live with a full dashboard. No terminal, no config files, just plug in your API key.
Download ArmaraOS — ainativelang.com macOS · Windows · Linux — free to start
Autonomous agents, 7 pre-built Hands (researcher, lead gen, clip editor, and more), 40 channel adapters (Telegram, Discord, Slack, WhatsApp…), 27 LLM providers, 16 security layers — all in a single ~32 MB binary.
Already have an AI agent? Add AINL in one command.
AINL installs directly into OpenClaw, ZeroClaw, Hermes, Claude Code, and any MCP-compatible agent. After install your agent can author, validate, and run deterministic workflows — with the largest token wins when you are still on LLM prompt-loop orchestration (baseline A below).
| Your agent | Install command | How-to guide |
|---|---|---|
| OpenClaw | ainl install-mcp --host openclaw |
ainativelang.com/install |
| ZeroClaw | zeroclaw skills install https://github.com/sbhooley/ainativelang/tree/main/skills/ainl |
ainativelang.com/install |
| Hermes Agent | ainl install-mcp --host hermes |
ainativelang.com/install |
| Claude Code | pip install 'ainativelang[mcp]' → add ainl-mcp to MCP config |
ainativelang.com/mcp |
| Any MCP host | pip install 'ainativelang[mcp]' → run ainl-mcp (stdio) |
ainativelang.com/mcp |
After install, ask your agent: "Use AINL to build this workflow" — it compiles once, runs many times without re-spending tokens on orchestration when the workload was previously prompt-loop driven.
Primary product path: ArmaraOS — desktop agent OS with dashboard, Hands, scheduled ainl run, and MCP authoring. See docs/competitive/ARMARAOS_GTM.md.
Token savings — pick your baseline:
| Your baseline today | Typical AINL win | Worth it for tokens alone? |
|---|---|---|
| A. LLM re-prompts routing/state on every cron/webhook | ~90–95% fewer orchestration tokens on recurring monitors (BENCHMARK.md, benchmark_compile_once_run_many.py) |
Often yes |
| B. Hand-optimized scripts + LLM only at judgment gates | ~1.3–1.5× on routing tokens (token_savings_results.json) |
Usually no — consider audit, MCP safety, emit, or ArmaraOS |
| C. Pure deterministic runners (no LLM in loop) | ~0% | No |
Full honest filter: docs/competitive/WHEN_AINL_DOES_NOT_HELP.md · docs/competitive/VS_HAND_WRITTEN_RUNNER.md (five-axis comparison vs a hand-written Python runner — concedes the token point on baseline B).
| Workload (baseline A — prompt-loop today) | Typical savings | Reproducible via |
|---|---|---|
| Recurring monitors, digests, scheduled jobs | ~90–95% fewer orchestration tokens vs prompt loops | scripts/benchmark_compile_once_run_many.py → tooling/compile_once_run_many_results.json |
| Multi-step automations (LLM-first routing) | ~2–7× reduction vs LLM-first (A vs C); ~1.3–1.5× vs hand-optimized (B vs C) | scripts/benchmark_token_savings.py → tooling/token_savings_results.json |
| Authoring density (LLM-style verbose Python) | ~1.7× mean / up to 2.5× fewer source tokens vs verbose bas |